A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living
This research aims to describe pattern recognition models for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. The early anticipation of anomalies can improve the rate of disease prevention. Here we present different learning techniques for predicting abnormalities and behavioural trends in various user contexts. In this paper we described a Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs. The outcomes of t…
Achieving energy efficiency in data centers with a performance-guaranteed power aware routing
Nowadays, data centers are designed to offer the highest performance in case of high traffic load and peak utilisation of the network. However, in a realistic data center environment, the peak capacity of the network is rarely reached and the average utilisation of devices varies between 5% and 25% which results into a huge loss of energy since most of the time links and servers are idle or under-utilized. The high impact of this wasted power on environmental effects, energy needs and electricity costs raised the concerns to seek for an efficient solution to make data centers more power effective while keeping the desired quality of service. In this paper, we propose a power-aware routing a…
LaCoDa: Layered connected topology for massive data centers
One of the fundamental challenges of existing data centers is to design a network that interconnects massive number of servers, and therefore providing an efficient and fault-tolerant routing service to upper-layer applications. Several solutions have been proposed (e.g. FatTree, DCell and BCube), however they either scale too fast (i.e., double exponentially) or too slow. This paper proposes a new data center topology, called LaCoDa, that combines the advantages of previous topologies while avoiding their limitations. LaCoDa uses a small node degree that matches physical restriction for servers, and it also interconnects a large number of servers while reducing the wiring complexity and wi…
An Energy Saving Mechanism Based on Vacation Queuing Theory in Data Center Networks
To satisfy the growing need for computing resources, data centers consume a huge amount of power which raises serious concerns regarding the scale of the energy consumption and wastage. One of the important reasons for such energy wastage relates to the redundancies. Redundancies are defined as the backup routing paths and unneeded active ports implemented for the sake of load balancing and fault tolerance. The energy loss may also be caused by the random nature of incoming packets forcing nodes to stay powered on all the times to await for incoming tasks. This paper proposes a re-architecturing of network devices to address energy wastage issue by consolidating the traffic arriving from di…